WO2016159949A1 - Analyseur d'application pour le cloud computing - Google Patents

Analyseur d'application pour le cloud computing Download PDF

Info

Publication number
WO2016159949A1
WO2016159949A1 PCT/US2015/023276 US2015023276W WO2016159949A1 WO 2016159949 A1 WO2016159949 A1 WO 2016159949A1 US 2015023276 W US2015023276 W US 2015023276W WO 2016159949 A1 WO2016159949 A1 WO 2016159949A1
Authority
WO
WIPO (PCT)
Prior art keywords
container
application
model
policy
given application
Prior art date
Application number
PCT/US2015/023276
Other languages
English (en)
Inventor
Chandra H. Kamalakantha
Parag Doshi
Reinier J. Aerdts
Original Assignee
Hewlett Packard Enterprise Development Lp
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Hewlett Packard Enterprise Development Lp filed Critical Hewlett Packard Enterprise Development Lp
Priority to PCT/US2015/023276 priority Critical patent/WO2016159949A1/fr
Priority to US15/506,400 priority patent/US10282171B2/en
Publication of WO2016159949A1 publication Critical patent/WO2016159949A1/fr

Links

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F8/00—Arrangements for software engineering
    • G06F8/10—Requirements analysis; Specification techniques
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F8/00—Arrangements for software engineering
    • G06F8/60—Software deployment
    • G06F8/61—Installation
    • G06F8/63—Image based installation; Cloning; Build to order
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F15/00—Digital computers in general; Data processing equipment in general
    • G06F15/16—Combinations of two or more digital computers each having at least an arithmetic unit, a program unit and a register, e.g. for a simultaneous processing of several programs
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F8/00—Arrangements for software engineering
    • G06F8/60—Software deployment
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F9/00—Arrangements for program control, e.g. control units
    • G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
    • G06F9/44—Arrangements for executing specific programs
    • G06F9/455—Emulation; Interpretation; Software simulation, e.g. virtualisation or emulation of application or operating system execution engines
    • G06F9/45533—Hypervisors; Virtual machine monitors
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F9/00—Arrangements for program control, e.g. control units
    • G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
    • G06F9/46—Multiprogramming arrangements
    • G06F9/50—Allocation of resources, e.g. of the central processing unit [CPU]
    • G06F9/5061—Partitioning or combining of resources
    • G06F9/5077—Logical partitioning of resources; Management or configuration of virtualized resources
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F9/00—Arrangements for program control, e.g. control units
    • G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
    • G06F9/46—Multiprogramming arrangements
    • G06F9/54—Interprogram communication
    • G06F9/547—Remote procedure calls [RPC]; Web services
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F9/00—Arrangements for program control, e.g. control units
    • G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
    • G06F9/44—Arrangements for executing specific programs
    • G06F9/455—Emulation; Interpretation; Software simulation, e.g. virtualisation or emulation of application or operating system execution engines
    • G06F9/45533—Hypervisors; Virtual machine monitors
    • G06F9/45558—Hypervisor-specific management and integration aspects
    • G06F2009/45562—Creating, deleting, cloning virtual machine instances
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F9/00—Arrangements for program control, e.g. control units
    • G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
    • G06F9/44—Arrangements for executing specific programs
    • G06F9/455—Emulation; Interpretation; Software simulation, e.g. virtualisation or emulation of application or operating system execution engines
    • G06F9/45533—Hypervisors; Virtual machine monitors
    • G06F9/45558—Hypervisor-specific management and integration aspects
    • G06F2009/4557—Distribution of virtual machine instances; Migration and load balancing
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F9/00—Arrangements for program control, e.g. control units
    • G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
    • G06F9/44—Arrangements for executing specific programs
    • G06F9/455—Emulation; Interpretation; Software simulation, e.g. virtualisation or emulation of application or operating system execution engines
    • G06F9/45533—Hypervisors; Virtual machine monitors
    • G06F9/45558—Hypervisor-specific management and integration aspects
    • G06F2009/45595—Network integration; Enabling network access in virtual machine instances

Definitions

  • System-level software virtualization is commonly employed in virtual hosting environments, where it is useful for securely allocating finite hardware resources amongst a large number of users and their respective applications.
  • system-level software virtualization provides a method where the kernel of an operating system allows for multiple isolated user space instances, instead of just one.
  • instances such as containers, virtualization engines (VE), or virtual private servers (VPS), for example, may operate like a monolithic server from the point of view of its owners and users yet at the same time is virtualized to a much smaller server footprint via available container technologies.
  • FIG. 1 illustrates an example of a system to automatically generate container or non-container servers for a cloud computing environment.
  • FIG. 2 illustrates an example of a system to automatically generate and manage container or non-container servers in a cloud computing environment.
  • FIG. 3 illustrates an example of an application model that can be employed with a system to generate container or non-container servers for a cloud computing environment.
  • FIG. 4 illustrates an example of an output model that can be generated from the application model depicted in FIG. 3.
  • FIG. 5 illustrates an example of a computer readable medium having machine-readable instructions to automatically generate container or non-container servers for a cloud computing environment.
  • FIG. 6 illustrates an example of a method to automatically generate container or non-container servers for a cloud computing environment.
  • This disclosure relates to an application analyzer that operates in conjunction with a deployment controller to facilitate deployment and lifecycle management for container-based or non-container based servers in a cloud computing environment.
  • Containers allow system designers to scale many applications into smaller computing footprints which can save on operating system costs.
  • moving a given application to a container may be a time-consuming task requiring significant expertise that is typically not available to frontline information technology designers.
  • the systems and methods disclosed herein provide a policy framework where a given application can be automatically analyzed with respect to one or more policies for its respective suitability (or non- suitability) to be implemented as a container.
  • a system includes a policy manager that includes a policy (or policies) to describe policy attributes of an application that define whether the application can be deployed as a container server or as a non-container server, for example.
  • the application analyzer analyzes a given application with respect to the policy attributes to classify the given application (e.g., container, non-container, type of container, and so forth).
  • Policy attributes can be globally or narrowly specified to characterize the attributes of an application with respect to its suitability to be containerized. For instance, a given application can be classified as fitting to a container model or a non-container model based on an analysis of the policy attributes with respect to analyzed application attributes of the given application.
  • the container model further can include container parameters for the given application based on the analysis.
  • a deployment controller generates a corresponding container server for the given application if the given application is classified as a container model.
  • the container server that is generated further can be established according to the container parameters determined by the application analyzer and provided with the model.
  • the deployment controller generates a
  • Automated learning can be utilized to update the policies in the policy manager as new applications are developed and deployed.
  • a lifecycle manager can also be provided with the deployment controller to manage the lifecycle of the deployed container or non-container servers, where lifecycle can include application installation/de-installation, upgrading, scaling up or down, enhancing security, monitoring, metering, and so forth.
  • FIG. 1 illustrates an example of a system 100 to automatically generate container or non-container servers for a cloud computing system 170.
  • cloud computing can include storage, computing, and network resources and capabilities that are that can be available as services along with other cloud services, such as platform services and applications.
  • the cloud computing system 170 can be hosted on a network, such as a public network ⁇ e.g., the Internet), a private network, a managed network environment, or a combination of different network types.
  • the memory resource and the processing resource could be stored a single machine (e.g., a computer) or distributed across multiple computers (via a network).
  • the system 100 includes a policy manger 1 10 that includes a policy 120 (or policies) to describe policy attributes of an application that define whether the application can be deployed as a container server or as a non-container server, for example.
  • An application analyzer 130 analyzes a given application 140 with respect to the policy attributes enumerated in the policy 120 to classify the given application.
  • Example classifications can include classifying the given application 140 as suitable for a container, not suitable for a container, or a specific type of container may be classified to facilitate application and/or container performance.
  • the term container refers to a self-contained application that includes substantially all components for application execution within the container and runs independently of operating system type. This is in contrast to a virtual machine or physical server referred to as non-containers that are highly operating system dependent and are installed with the necessary interfaces to interact with the resident operating system.
  • Output from the application analyzer 130 includes a model 150.
  • the model 150 denotes whether or not the application 140 can be containerized and/or what specific type of container (or non-container, such as a virtual machine) to employ.
  • Container type examples can include containers provided by Docker, Origin (Open Shift), and Cloud Foundry, for example.
  • the model 150 is supplied to a deployment controller 160 which then generates the type of server specified in the model and deploys the server to a computing cloud 170 which can include one or more computers that support the cloud.
  • Policy attributes for the policy 120 can be globally or narrowly specified to characterize the attributes of an application with respect to its suitability to be containerized.
  • the application 140 and its basic attributes can include for example, application runtime stack (e.g., app server, database server, cache server), followed by runtime requirements (e.g., RAM, disk, and so forth), service level requirements, environment requirements (development, test, pre-production, and so forth).
  • the analyzer 130 thus can determine these and other related attributes and provide a model for subsequent use by the deployment controller 160 (See e.g., model specifier and generator in FIG. 2).
  • An example global policy may be specified as "All applications below a given service level requirement (e.g., threshold specifying number of web pages server per minute) can be containerized.”
  • An example of a more narrow policy may be stated as "Any application from this vendor cannot be containerized.”
  • An example of a tenant-based policy may be specified as "All applications submitted from Tenant A are to be placed in a container, unless infeasible.”
  • An example of a tenant-based policy may be specified as "All Applications for tenant B are to be run on a virtual server or physical machine.”
  • the application analyzer 130 can classify a given application 140 as fitting to a container model or a non-container model based on an analysis of the policy attributes with respect to analyzed application attributes of the given application 140. In some cases, only a single attribute may be analyzed by the application analyzer 130 and in other cases multiple attributes may be analyzed including application type, service level performance, tenant preferences, application suitability/ unsuitability, deployment considerations, legal restrictions, and so forth. If the application 140 is determined to be suitable for a container, the application analyzer 130 constructs the model 150, which the deployment controller 160 then employs to generate a corresponding container server for the given application and distributes and manages it with respect to the cloud 170.
  • the deployment controller 160 If the given application 140 is not suitable for containers based on the model 150 specification, the deployment controller 160 generates a corresponding non-container server for the given application based on the non-container model 150 generated for the given application .
  • Automated learning can be provided (See e.g., FIG. 2) to update the policies 120 in the policy manager 1 10 as new applications are developed and deployed. Policies can also be updated manually as system conditions change (e.g., customer preferences, service level agreements, billing matters, and so forth).
  • a lifecycle manager See e.g., FIG. 2) can also be provided with the deployment controller 160 to manage the lifecycle of the deployed container or non-container servers.
  • lifecycle for deployed server can include application installation/de-installation, upgrading, scaling up or down, enhancing security, monitoring, metering, and so forth.
  • container technologies have become more popular in the last few years, where containers allow code, applications, and other runtime components to be packaged in highly portable packages. Containers do not depend on whether they are hosted on physical or virtual machines nor the type of operating systems required. The fundamental distinction between non-container vs. container-based technology stacks is that non-container based solutions rely on intensive operating system support, for example. As a result, solutions employing containers
  • the application 140 and it is characteristics can be captured initially as a model (e.g., unified modeling language, See e.g., FIG. 3).
  • This captured model is then processed by the application analyzer 130 to determine the application fit to be hosted in container versus non-container technologies where model output is then generated at 150 specifying whether or not a container has been selected or what type of container, or what type of non-container technology should be employed such as a virtual machine or physical machine specification.
  • the application analyzer 130 analyzes container choices versus non-container based technology differences.
  • example containers can include Docker, Origin (OpenShift), Cloud Foundry, and so forth.
  • the system 100 can operate in accordance with container cluster
  • management software or individual containers can be supported depending on providers that are registered within the deployment controller 160, for example.
  • FIG. 2 illustrates an example of a system 200 to automatically generate and manage container or non-container servers in a cloud computing environment.
  • the system 200 includes an application analyzer 202 having a model specifier 204 (e.g., graphical user interface and model processing machine readable instructions) which is employed to define policy attributes for a given application 208.
  • Inputs to the model specifier 204 can include provider data 212, tenancy data 214, catalog data 216, offerings and design data 218, and telemetry data 220 which are described in more detail below.
  • Output from the model specifier 204 See e.g., application model diagram FIG.
  • the deployment controller 250 includes a lifecycle manager 260 to manage to manage lifecycle issues with the deployed container or non-container server.
  • lifecycle management can include application installation/de-installation, upgrading, scaling up or down, enhancing security, monitoring, metering, and so forth.
  • application loading can be monitored via the installed container servers. If the load is more or less than when installed, additional servers and/or containers can be added or removed from service to support the determined load.
  • containers or non-container servers
  • the deployment controller 250 can also include a server generator 270 which generates the code to create a corresponding server (e.g., a container server or a non-container server) based on the output model 240 that is provided by the model generator 230 of the application analyzer 202 for the given application 208.
  • a server generator 270 can call functions in Docker software to instantiate a Docker container for the given application 208.
  • the server generator 270 can call the respective software to generate that type of container.
  • a policy manager 280 includes a policy 284 (or policies) that describe attributes of an application that are analyzed by the server generator 270.
  • the policy 284 can receive updates from the model specifier 204 regarding changes with respect to the inputs 212-220.
  • the policy 284 further can set rules employed to establish parameters for the model 240 that is being
  • the policy manager 280 can include a learning component 290 which can be employed to learn and determine which applications can be containerized and which cannot. For example, if a new application is analyzed and it is determined that it is a fit for a container, the policy 284 can automatically be updated via the learning component 290 that such application in the future is a suitable candidate for a container.
  • the learning component 290 can include code programmed to implement substantially any type of artificial intelligence component, such as a classifier (e.g., support vector machine).
  • a specific example for the learning component 290 includes utilization of a Resource Description Framework (RDF) component.
  • RDF Resource Description Framework
  • the system 200 and learning component 290 utilizes RDF and Web Ontology Language (OWL).
  • the RDF has features that facilitate data merging even if the underlying schemas differ, and it specifically supports the evolution of schemas over time without requiring all the data
  • the RDF extends the linking structure of the Web to use URIs to name the relationship between things as well as the two ends of the link (usually referred to as a "triple"). Using this model, it allows structured and semi- structured data to be mixed, exposed, and shared across different applications.
  • This linking structure forms a directed, labeled graph, where the edges represent the named link between two resources, represented by the graph nodes.
  • the learning component 290 builds the triples of application components that could be
  • the model specifier 204 receives various inputs 212-220 that enable application models to be developed and policies attributes of the policy 284 to be defined.
  • the provider data 212 provides the capability for the deployment engineer to describe the available deployment environments (e.g., available resources) along with credentials and API (application interface) end points to automatically create the server or container.
  • the server could be a virtual machine or physical server with cluster support, for example.
  • Tenancy data 214 allows multi- tenancy support. The tenancy data 214 thus allows for the deployment engineer to setup tenants and their related deployment environments, which can be set according service level agreements between each tenant and their subscribers.
  • Catalog data 216 leverages the application (See e.g., FIG. 4) followed by which clients can provision the application.
  • application design, its related artifacts and offerings can be published as a higher level entity for tenants of a service provider to access and provision it. It also provides an interface to capture cost, how it is monitored or metered for providing both usage/consumption based billing and flat rate billing methods to instrument telemetry, for example.
  • the model specifier 202 can update the policy accordingly. For example, if an intensive level of monitoring is required to guarantee a given service level, it may not be possible to containerize the given application 208.
  • the service design and offering data 218 allows the deployment engineer to describe the application and its persona.
  • the example of such capability is described in FIG. 4 that describes an application design which can be referred to as a service design.
  • the service offering is an instance of the service design that is specific to a given tenant.
  • the service design of a given application such as depicted in FIG. 4 is generic however, instance characteristics of it varies by tenant.
  • Vendor A may run the application depicted in FIG. 4 on containers because of policy constraints and Vendor B may run the application on physical servers, for example.
  • the telemetry data 220 supports various billing strategies for the system such as billing by consumption/usage, provisioning, business value pricing, and so forth.
  • the telemetry data 220 can be implemented as an abstract interface (in object oriented terms) and supports multiple implementations for different billing strategies. For example it can support billing in the given application based on number of help desk tickets processed or flat rate billing which can influence whether or not the given application 208 can be containerized.
  • the policy manager 280 provides capability to describe/register the types of application that can be containerized by a tenant. For example, application or web servers such Apache, TomCat, and so forth are container aware however the applications that run on it may not be because of need of security.
  • the policy manager 280 and policy 284 can automatically be enriched by self-learning by use of machine learning technology in the learning component 290 (e.g., Resource Description Framework - RDF).
  • the example model 400 depicted in FIG. 4 leverages the policy manager 280 to bind the appropriate application to a server (e.g., virtual machine, Physical Server, or Container) by using policies 284.
  • a server e.g., virtual machine, Physical Server, or Container
  • the server generator 270 uses a Docker API to create Docker container and VMware API to create a virtual machine on VMware or IPMI to create a new physical server that is registered under the providers at 212.
  • FIG. 3 illustrates an example of an application model 300 that can be employed by deployment controller (e.g., 160 or 250) to generate container or non- container servers for a cloud computing environment.
  • the application model 300 represents but one of a plurality of differing configurations to support a given cloud computing service.
  • the model for a web site application is described having an application URL 310 that provides access to a given web page.
  • a load balancer 320 may be utilized to manage the load across various servers that may support the application model 300.
  • the servers can include a database server 330, and application/web server 340, and/or a cache server 350.
  • Each of the respective servers 330-350 can be implemented as a container or non-container technology depending on the underlying policy and automated analysis described herein.
  • the model 300 can then be processed by an application analyzer and model generator described herein producing an example output such as depicted in FIG. 4.
  • FIG. 4 illustrates an example of an output model 400 that can be generated from the application model depicted in FIG. 3.
  • the model 400 depicts that all of application components of FIG. 3 are first designated as an abstract server denoting that a unique server instantiation is required.
  • the abstract server can then be dynamically loaded to providers as corresponding container or non-container servers at runtime using the runtime environment of the deployment controller, for example.
  • the corresponding server can bind to the resource providers that are described by the model specifier described above.
  • the model 400 includes an application URL 404 and load balancer node 410.
  • a branch A branch
  • representing a database server includes one or more database components 414 (e.g., SQL components) and one or more database servers 416 which are initially bound to an abstract server 418.
  • database components 414 e.g., SQL components
  • database servers 416 which are initially bound to an abstract server 418.
  • the deployment controller (e.g., 160 or 250) generates the server type based on a specification provided in an abstract server type at 420.
  • the abstract server type 420 can specify a container, container type, virtual machine, or physical server, for example, and automatically determined via the policies and analytics described herein.
  • Another branch of the model 400 supporting web server operations includes one or more web server components 424 supported by one or more web application servers 426 which are bound to an abstract server 428. Again, the server type for the abstract server 428 is specified at 430.
  • a third branch of the diagram includes a cache server 434 that runs on abstract server 436 where its type is specified at 438.
  • FIG. 5 illustrates an example of a computer readable medium 500 having machine-readable instructions to automatically generate container or non-container servers for a cloud computing environment.
  • the instructions can be configured via the various functional blocks represented in the medium 500.
  • These block include a policy manager 510 to specify a policy 520 to describe policy attributes of an application that define whether the application can be deployed as a container server or as a non-container server via model 530.
  • An input analyzer block 540 analyzes a given application with respect to the policy attributes of the policy 520 to classify the given application as a container model or a non-container model.
  • a deployment controller block 550 includes instructions to generate a corresponding container server for the given application if the given application is classified as a container model or generate a corresponding non-container server for the given application if the given application is classified as a non-container model.
  • the medium 500 can also include instructions to support other functions described herein including lifecycle management and learning, for example.
  • FIG. 6 illustrates an example of a method 600 to automatically generate container or non-container servers for a cloud computing environment.
  • the method 600 includes specifying a policy to describe policy attributes of an
  • the method 600 includes analyzing a given application with respect to the policy attributes to classify the given application as a container model or a non-container model (e.g., via application analyzer 130 of FIG. 1 ).
  • the method 600 includes generating a corresponding container server for the given application if the given application is classified as a container model or generating a corresponding non-container server for the given application if the given application is classified as a non-container model (e.g., via deployment controller 160 of FIG. 1 ).
  • the method 600 includes managing a lifecycle of the given application in the

Landscapes

  • Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • Software Systems (AREA)
  • General Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Computer Hardware Design (AREA)
  • Stored Programmes (AREA)
  • Debugging And Monitoring (AREA)

Abstract

Dans cette invention, un système comprend un gestionnaire de politique qui inclut une politique destinée à décrire des attributs de politique d'une application qui définissent si l'application peut être déployée sous la forme d'un serveur contenant ou d'un serveur non-contenant. Un analyseur d'application analyse une application donnée sur le plan de ses attributs de politique afin de classer ladite application parmi les modèles contenants ou les modèles non-contenants. Un contrôleur de déploiement génère un serveur contenant correspondant pour cette application si ladite application est classée parmi les modèles contenants, ou génère un serveur non-contenant correspondant pour l'application si elle est classée parmi les modèles non-contenants.
PCT/US2015/023276 2015-03-30 2015-03-30 Analyseur d'application pour le cloud computing WO2016159949A1 (fr)

Priority Applications (2)

Application Number Priority Date Filing Date Title
PCT/US2015/023276 WO2016159949A1 (fr) 2015-03-30 2015-03-30 Analyseur d'application pour le cloud computing
US15/506,400 US10282171B2 (en) 2015-03-30 2015-03-30 Application analyzer for cloud computing

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
PCT/US2015/023276 WO2016159949A1 (fr) 2015-03-30 2015-03-30 Analyseur d'application pour le cloud computing

Publications (1)

Publication Number Publication Date
WO2016159949A1 true WO2016159949A1 (fr) 2016-10-06

Family

ID=57007060

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/US2015/023276 WO2016159949A1 (fr) 2015-03-30 2015-03-30 Analyseur d'application pour le cloud computing

Country Status (2)

Country Link
US (1) US10282171B2 (fr)
WO (1) WO2016159949A1 (fr)

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US10565092B2 (en) 2017-10-06 2020-02-18 Red Hat, Inc. Enabling attributes for containerization of applications
US10637783B2 (en) 2017-07-05 2020-04-28 Wipro Limited Method and system for processing data in an internet of things (IoT) environment
WO2020186899A1 (fr) * 2019-03-19 2020-09-24 华为技术有限公司 Procédé et appareil d'extraction de métadonnées dans un processus d'entraînement d'apprentissage automatique
US20240036845A1 (en) * 2022-07-26 2024-02-01 Red Hat, Inc. Runtime environment optimizer for jvm-style languages

Families Citing this family (53)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US10977260B2 (en) 2016-09-26 2021-04-13 Splunk Inc. Task distribution in an execution node of a distributed execution environment
US11461334B2 (en) 2016-09-26 2022-10-04 Splunk Inc. Data conditioning for dataset destination
US11243963B2 (en) 2016-09-26 2022-02-08 Splunk Inc. Distributing partial results to worker nodes from an external data system
US11874691B1 (en) 2016-09-26 2024-01-16 Splunk Inc. Managing efficient query execution including mapping of buckets to search nodes
US11222066B1 (en) 2016-09-26 2022-01-11 Splunk Inc. Processing data using containerized state-free indexing nodes in a containerized scalable environment
US10956415B2 (en) 2016-09-26 2021-03-23 Splunk Inc. Generating a subquery for an external data system using a configuration file
US11663227B2 (en) 2016-09-26 2023-05-30 Splunk Inc. Generating a subquery for a distinct data intake and query system
US11580107B2 (en) 2016-09-26 2023-02-14 Splunk Inc. Bucket data distribution for exporting data to worker nodes
US11281706B2 (en) 2016-09-26 2022-03-22 Splunk Inc. Multi-layer partition allocation for query execution
US11416528B2 (en) 2016-09-26 2022-08-16 Splunk Inc. Query acceleration data store
US10353965B2 (en) 2016-09-26 2019-07-16 Splunk Inc. Data fabric service system architecture
US11567993B1 (en) 2016-09-26 2023-01-31 Splunk Inc. Copying buckets from a remote shared storage system to memory associated with a search node for query execution
US11604795B2 (en) 2016-09-26 2023-03-14 Splunk Inc. Distributing partial results from an external data system between worker nodes
US11232100B2 (en) 2016-09-26 2022-01-25 Splunk Inc. Resource allocation for multiple datasets
US11023463B2 (en) 2016-09-26 2021-06-01 Splunk Inc. Converting and modifying a subquery for an external data system
US11550847B1 (en) 2016-09-26 2023-01-10 Splunk Inc. Hashing bucket identifiers to identify search nodes for efficient query execution
US11321321B2 (en) 2016-09-26 2022-05-03 Splunk Inc. Record expansion and reduction based on a processing task in a data intake and query system
US11269939B1 (en) 2016-09-26 2022-03-08 Splunk Inc. Iterative message-based data processing including streaming analytics
US11314753B2 (en) 2016-09-26 2022-04-26 Splunk Inc. Execution of a query received from a data intake and query system
US11250056B1 (en) 2016-09-26 2022-02-15 Splunk Inc. Updating a location marker of an ingestion buffer based on storing buckets in a shared storage system
US11599541B2 (en) 2016-09-26 2023-03-07 Splunk Inc. Determining records generated by a processing task of a query
US11294941B1 (en) 2016-09-26 2022-04-05 Splunk Inc. Message-based data ingestion to a data intake and query system
US11163758B2 (en) 2016-09-26 2021-11-02 Splunk Inc. External dataset capability compensation
US11126632B2 (en) 2016-09-26 2021-09-21 Splunk Inc. Subquery generation based on search configuration data from an external data system
US20180089324A1 (en) 2016-09-26 2018-03-29 Splunk Inc. Dynamic resource allocation for real-time search
US10984044B1 (en) 2016-09-26 2021-04-20 Splunk Inc. Identifying buckets for query execution using a catalog of buckets stored in a remote shared storage system
US11615104B2 (en) 2016-09-26 2023-03-28 Splunk Inc. Subquery generation based on a data ingest estimate of an external data system
US11586627B2 (en) 2016-09-26 2023-02-21 Splunk Inc. Partitioning and reducing records at ingest of a worker node
US11620336B1 (en) 2016-09-26 2023-04-04 Splunk Inc. Managing and storing buckets to a remote shared storage system based on a collective bucket size
US11442935B2 (en) 2016-09-26 2022-09-13 Splunk Inc. Determining a record generation estimate of a processing task
US11003714B1 (en) 2016-09-26 2021-05-11 Splunk Inc. Search node and bucket identification using a search node catalog and a data store catalog
US11593377B2 (en) 2016-09-26 2023-02-28 Splunk Inc. Assigning processing tasks in a data intake and query system
US11106734B1 (en) * 2016-09-26 2021-08-31 Splunk Inc. Query execution using containerized state-free search nodes in a containerized scalable environment
US11860940B1 (en) 2016-09-26 2024-01-02 Splunk Inc. Identifying buckets for query execution using a catalog of buckets
US11562023B1 (en) 2016-09-26 2023-01-24 Splunk Inc. Merging buckets in a data intake and query system
US11921672B2 (en) 2017-07-31 2024-03-05 Splunk Inc. Query execution at a remote heterogeneous data store of a data fabric service
US11989194B2 (en) 2017-07-31 2024-05-21 Splunk Inc. Addressing memory limits for partition tracking among worker nodes
CN109525624B (zh) * 2017-09-20 2022-01-04 腾讯科技（深圳）有限公司 一种容器登录方法、装置及存储介质
US11151137B2 (en) 2017-09-25 2021-10-19 Splunk Inc. Multi-partition operation in combination operations
US10896182B2 (en) 2017-09-25 2021-01-19 Splunk Inc. Multi-partitioning determination for combination operations
US10552188B2 (en) * 2017-11-01 2020-02-04 Alibaba Group Holding Limited Virtual private cloud services with physical machine servers and virtual machines
US11334543B1 (en) 2018-04-30 2022-05-17 Splunk Inc. Scalable bucket merging for a data intake and query system
CN109146084B (zh) * 2018-09-06 2022-06-07 郑州云海信息技术有限公司 一种基于云计算的机器学习的方法及装置
US11221837B2 (en) 2019-04-11 2022-01-11 Microsoft Technology Licensing, Llc Creating and deploying packages to devices in a fleet based on operations derived from a machine learning model
US11029936B2 (en) * 2019-04-11 2021-06-08 Microsoft Technology Licensing, Llc Deploying packages to devices in a fleet in stages
WO2020220216A1 (fr) 2019-04-29 2020-11-05 Splunk Inc. Estimation de temps de recherche dans un système d'entrée et d'interrogation de données
US11715051B1 (en) 2019-04-30 2023-08-01 Splunk Inc. Service provider instance recommendations using machine-learned classifications and reconciliation
US11494380B2 (en) 2019-10-18 2022-11-08 Splunk Inc. Management of distributed computing framework components in a data fabric service system
US11182196B2 (en) * 2019-11-13 2021-11-23 Vmware, Inc. Unified resource management for containers and virtual machines
US11922222B1 (en) 2020-01-30 2024-03-05 Splunk Inc. Generating a modified component for a data intake and query system using an isolated execution environment image
US11704313B1 (en) 2020-10-19 2023-07-18 Splunk Inc. Parallel branch operation using intermediary nodes
US11733974B2 (en) * 2021-06-15 2023-08-22 HCL America Inc. Method and system for automatically creating instances of containerized servers
CN115033348B (zh) * 2022-08-10 2022-10-25 北京腾达泰源科技有限公司 一种对虚拟机和容器统一管理方法、系统、设备及介质

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20030187946A1 (en) * 2002-03-15 2003-10-02 Laurence Cable System and method for automatically partitioning an application between a web server and an application server
US20050267856A1 (en) * 2004-05-19 2005-12-01 Bea Systems, Inc. System and method for application container architecture
US20100257527A1 (en) * 2009-04-01 2010-10-07 Soluto Ltd Computer applications classifier
US20110213875A1 (en) * 2010-02-26 2011-09-01 James Michael Ferris Methods and Systems for Providing Deployment Architectures in Cloud Computing Environments
US8621069B1 (en) * 2010-09-03 2013-12-31 Adobe Systems Incorporated Provisioning a computing application executing on a cloud to a client device

Family Cites Families (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US7966814B2 (en) * 2005-06-01 2011-06-28 Emmanuel Buis Internal combustion engine control method
US20070198973A1 (en) 2006-02-02 2007-08-23 International Business Machines Corporation Computer-implemented method, system, and program product for deployment time optimization of a distributed application
US20110088011A1 (en) 2009-10-14 2011-04-14 Vermeg Sarl Automated Enterprise Software Development
US8918448B2 (en) 2012-05-11 2014-12-23 International Business Machines Corporation Application component decomposition and deployment
US9509553B2 (en) 2012-08-13 2016-11-29 Intigua, Inc. System and methods for management virtualization
US9122562B1 (en) * 2014-06-19 2015-09-01 Amazon Technologies, Inc. Software container recommendation service
US9465590B2 (en) * 2014-07-07 2016-10-11 Sap Se Code generation framework for application program interface for model
US9575797B2 (en) * 2015-03-20 2017-02-21 International Business Machines Corporation Virtual machine migration between hypervisor virtual machines and containers

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20030187946A1 (en) * 2002-03-15 2003-10-02 Laurence Cable System and method for automatically partitioning an application between a web server and an application server
US20050267856A1 (en) * 2004-05-19 2005-12-01 Bea Systems, Inc. System and method for application container architecture
US20100257527A1 (en) * 2009-04-01 2010-10-07 Soluto Ltd Computer applications classifier
US20110213875A1 (en) * 2010-02-26 2011-09-01 James Michael Ferris Methods and Systems for Providing Deployment Architectures in Cloud Computing Environments
US8621069B1 (en) * 2010-09-03 2013-12-31 Adobe Systems Incorporated Provisioning a computing application executing on a cloud to a client device

Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US10637783B2 (en) 2017-07-05 2020-04-28 Wipro Limited Method and system for processing data in an internet of things (IoT) environment
US10565092B2 (en) 2017-10-06 2020-02-18 Red Hat, Inc. Enabling attributes for containerization of applications
WO2020186899A1 (fr) * 2019-03-19 2020-09-24 华为技术有限公司 Procédé et appareil d'extraction de métadonnées dans un processus d'entraînement d'apprentissage automatique
US20240036845A1 (en) * 2022-07-26 2024-02-01 Red Hat, Inc. Runtime environment optimizer for jvm-style languages
US11972242B2 (en) * 2022-07-26 2024-04-30 Red Hat, Inc. Runtime environment optimizer for JVM-style languages

Also Published As

Publication number Publication date
US20180225095A1 (en) 2018-08-09
US10282171B2 (en) 2019-05-07

Similar Documents

Publication Publication Date Title
US10282171B2 (en) Application analyzer for cloud computing
US11743144B2 (en) Systems and methods for domain-driven design and execution of metamodels
Tomarchio et al. Cloud resource orchestration in the multi-cloud landscape: a systematic review of existing frameworks
US9754303B1 (en) Service offering templates for user interface customization in CITS delivery containers
US9612815B1 (en) Method and tool for automating deployment of reference implementation architectures for pre-integrated multi-product solutions
US10983774B2 (en) Extensions for deployment patterns
EP3095031B1 (fr) Plateforme de gestion de décision basée sur l'infonuagique
US9967318B2 (en) Apparatus, systems, and methods for cloud agnostic multi-tier application modeling and deployment
US8612976B2 (en) Virtual parts having configuration points and virtual ports for virtual solution composition and deployment
US9311161B2 (en) Automatically configured management service payloads for cloud IT services delivery
Wettinger et al. Unified invocation of scripts and services for provisioning, deployment, and management of cloud applications based on TOSCA
Brogi et al. TosKer: a synergy between TOSCA and Docker for orchestrating multicomponent applications
US10284634B2 (en) Closed-loop infrastructure orchestration templates
US8839223B2 (en) Validation of current states of provisioned software products in a cloud environment
Armstrong et al. Towards energy aware cloud computing application construction
De Gouw et al. Declarative elasticity in ABS
Kousiouris et al. Functionalities, Challenges and Enablers for a Generalized FaaS based Architecture as the Realizer of Cloud/Edge Continuum Interplay.
Li et al. Tulsa: a tool for transforming UML to layered queueing networks for performance analysis of data intensive applications
Hirmer et al. TOSCA4Mashups: enhanced method for on-demand data mashup provisioning
Lebre et al. Vmplaces: A generic tool to investigate and compare vm placement algorithms
Hwang et al. FitScale: scalability of legacy applications through migration to cloud
Alwis et al. A multi-tenancy aware architectural framework for SaaS application development
Stankovski et al. Developing a Model Driven Approach for engineering applications based on mOSAIC: Towards sharing elastic components in the Cloud
Fortuna et al. On-premise artificial intelligence as a service for small and medium size setups
Mulahuwaish et al. Improving datacenter utilization through containerized service-based architecture

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 15887958

Country of ref document: EP

Kind code of ref document: A1

WWE Wipo information: entry into national phase

Ref document number: 15506400

Country of ref document: US

NENP Non-entry into the national phase

Ref country code: DE

122 Ep: pct application non-entry in european phase

Ref document number: 15887958

Country of ref document: EP

Kind code of ref document: A1